Salford Systems' training courses provide a firm grounding in the theory and methodology behind each of our products, as well as practical examples illustrating both the basics and the finer points of the software. We teach you tips and tricks to help you squeeze out extra insight from your data and keep you updated on all the latest developments.Come prepared to expand your knowledge and understanding of Salford's data mining tools in a three-day series of training courses that will cover the software's basic, advanced and revolutionary components.
About the Course Instructor
Mikhail Golovnya, M.S.
Salford Systems' Senior Scientist Mikhail Golovnya provides data mining consultation for projects and works in model development and the search for technological improvements to Salford’s core products. He is responsible for advanced testing and prototyping of new data mining algorithms and modeling automation. Along with leading extensive training sessions in CART®, MARS®, TreeNet®, RandomForests® and the Salford Predictive Modeler™ suite, Golovnya provides guidance and technical support to Salford Systems' data mining clients.
Sharpen your decision tree expertise during this one-day advanced course, geared towards analysts and modelers with prior knowledge of tree algorithms. Using case studies, seminar topics include:
What is MARS? Why does it work? How can it be used? How can it help you develop more accurate regression models for problems such as predicting credit card holder balances, insurance claim losses, customer catalog orders, and cell phone use?
RandomForests®, created by Leo Breiman and Adele Cutler, is based on learning ensembles of CART trees. By judiciously injecting randomness into the tree-building process and then combining hundreds of these trees, RF is able to deliver high performance predictive models and a variety of novel exploratory data analysis results. RF also incorporates new metric free CLUSTER analyses that automatically select the variables used to define each cluster, with potentially different variables defining each cluster.
Attendees will be introduced to the main concepts in boosting methods in data mining. They will also be presented with the core innovations behind TreeNet stochastic gradient boosting, including the concepts of slow learning, use of weak learners in every stage of model building, resampling from the training at every stage, and ignoring data considered too far from the decision boundary in classification problems.